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Distributed caching spreads data across multiple servers for better performance and reliability.

Monolithic and microservices architectures each have unique advantages and challenges for software development.

Elasticsearch uses distributed architecture to search data quickly across multiple servers.

FAANG engineers use high-level design principles to create scalable systems.

ZooKeeper coordinates distributed systems using an ensemble of servers and the ZAB protocol.

Microservices are useful when you need independent scaling, deployment, and clear boundaries in software design.

Vertical scaling means upgrading one server, while horizontal scaling means adding more servers.

Choosing the right boundaries for microservices is crucial for effective architecture.

Database replication enhances data availability and read performance but has challenges like replication lag.

Database per Service ensures each microservice owns its data, reducing dependencies.

Microservices can communicate using five key patterns based on their needs.

Kafka Schema Evolution allows safe changes to message structures without breaking existing consumers.

Stateless servers are easier to scale because they don't store user session data locally.

Dependency failures can disrupt applications, but resilience patterns can help manage them.

Search engines collect, index, retrieve, and rank documents to provide relevant results.

Microservices and modular monoliths differ mainly in deployment and communication methods.

Choosing the right distributed lock depends on your system's needs and existing tools.

The Saga Pattern manages distributed transactions using local transactions and compensating actions.

Distributed locks ensure only one server performs a task, avoiding duplication.

Cache breakdowns can overwhelm databases, but request coalescing solves this issue.

The Bulkhead Pattern isolates resources to protect critical workloads from failures.

Elasticsearch CCR allows real-time data replication for faster disaster recovery.

Cascading failures occur when one service's failure impacts others, causing widespread issues.

The Transactional Outbox Pattern ensures database updates and event publishing are synchronized to avoid losing events.

Kafka uses different strategies to assign partitions to consumers in a group efficiently.

Elasticsearch uses shards for data distribution and replicas for redundancy.

Kafka consumers can use multiple threads to speed up message processing, but it complicates ordering and offset management.

An API Gateway simplifies client access to backend services in microservices.

In distributed systems, timestamps don't always reflect the true order of events.

Synchronous replication waits for confirmation from replicas, while asynchronous allows faster writes without waiting.

Choose Elasticsearch for complex text searches, but use databases for structured queries.

Database replication helps keep data available and allows systems to handle more read requests.

Database bottlenecks slow down performance, but identifying them helps optimize and scale effectively.

Message queues allow services to communicate asynchronously, improving scalability and reliability.

Connection pooling reuses database connections but can cause issues if misconfigured.

Distributed tracing helps track requests across microservices to find delays.